---
title: "Dashboard_test"
output: 
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    theme: spacelab
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```

```{r Loading libraries}
library(tidyverse)
library(stringr)
library(mapview)
library(maptools)
library(DT)
library(vegan)
library(plotly)

world<- rgdal::readOGR("datasources/world_eez_with_land/EEZ_land_v2_201410.shp", verbose = FALSE)
habitats <- readRDS("datasources/habitats.RDS")

```

Habitats dataset
=======================================================================

Column {data-height=700}
-----------------------------------------------------------------------

### All habitat data were intersected with Exclusive Economical Zone layer to calculate the area in ```km^2```, then were divided in coastal habitats and oceanic habitats. 
```{r}
datatable(
  habitats %>%
    select(Country, Zone, Habitat, Area),
  filter = 'top',
  rownames = FALSE
) %>%
  formatRound("Area", digits = 1)
```

Column {data-height=700}
-----------------------------------------------------------------------

### Richnes values are totals per each zone: Oceanic and Coastal, same for Shannon H: use filter or search to show different Countries and the next button below to scroll down the rows.
```{r Datatable diversity}
habitats %>%
  add_count(Country, Zone) %>%
  group_by(Country, Zone) %>%
  mutate(H = diversity(Area, index = "shannon", 2))  %>%
  select(Country, Zone, Richness = n, Shannon_H = H) %>%
  unique(.) -> rich

datatable(rich,
          filter = 'top',
          rownames = FALSE) %>%
  formatRound("Shannon_H", digits = 1)
```


Habitats charts
=======================================================================

Column {data-height=700}
-----------------------------------------------------------------------

### Countries by habitat richness

```{r fig.height=25}
habitats %>%
  group_by(Country) %>%
  tally() %>%
  select(Country, Richness = n) -> rich

p1 <- rich %>%
  ungroup() %>%
  arrange(-Richness) %>%
  #slice(., 1:20) %>%
  ggplot(aes(x = reorder(Country, Richness), y = Richness)) +
  geom_point(stat = 'identity', fill = "black", size = 4) +   # Draw points
  geom_segment(aes(
    x = Country,
    xend = Country,
    y = 0,
    yend = Richness
  ),
  color = "black") +   # Draw dashed lines
  labs(
    title = "Habitat richness",
    x = "",
    y = "Richness (N° of habitats)",
    caption = "source: hdc"
  ) +
  coord_flip()
ggplotly(p1)

```


Column {data-height=700}
-----------------------------------------------------------------------

### Countries by habitat diversity
```{r fig.height=25}
habitats %>%
  group_by(Country) %>%
  summarise(H = diversity(Area, index = "shannon", 2)) -> rich

p1 <- rich %>%
  ungroup() %>%
  arrange(-H) %>%
  #slice(., 1:20) %>%
  ggplot(aes(x = reorder(Country, H), y = H)) +
  geom_point(stat = 'identity', fill = "black", size = 4) +   # Draw points
  geom_segment(aes(
    x = Country,
    xend = Country,
    y = 0,
    yend = H
  ),
  color = "black") +   # Draw dashed lines
  labs(
    title = "Habitat diversity",
    x = "",
    y = "Shannon H",
    caption = "source: hdc"
  ) +
  coord_flip()
ggplotly(p1)

```


All habitats
=======================================================================

Column {data-height=700}
-----------------------------------------------------------------------


### Total habitat richness

```{r Habitat richness, fig.height=10}
# habitat richness / diversity per country
habitats %>%
  group_by(Country, Zone) %>%
  tally() %>%
  group_by(Country) %>%
  mutate(tot.rich = sum(n)) -> rich

rich_map <- merge(world, filter(rich, Zone == "Coastal"), by = "Country")

mapview(rich_map, zcol = "tot.rich")

```

Coastal habitats richness
=======================================================================

Column {data-height=700}
-----------------------------------------------------------------------

### Coastal habitat richness

```{r Coastal habitat richness, fig.height=10}
# habitat richness / diversity per country
habitats %>%
  group_by(Country, Zone) %>%
  tally() %>%
  group_by(Country) %>%
  mutate(tot.rich = sum(n)) -> rich


rich_map <- merge(world, filter(rich, Zone == "Coastal"), by = "Country")

mapview(rich_map, zcol = "n")
```

Oceanic habitats richness
=======================================================================

Column {data-height=700}
-----------------------------------------------------------------------

### Oceanic habitat richness

```{r Oceanic habitat richness, fig.height=10}
# habitat richness per country
habitats %>%
  group_by(Country, Zone) %>%
  tally() %>%
  group_by(Country) %>%
  mutate(tot.rich = sum(n)) -> rich


rich_map <- merge(world, filter(rich, Zone == "Oceanic"), by = "Country")


mapview(rich_map, zcol = "n")
```



Coastal habitats diversity
=======================================================================

Column {data-height=700}
-----------------------------------------------------------------------
### Coastal habitats diversity calculated with Shannon index (H)

```{r Coastal habitat diversity, fig.height=10}
# habitat diversity per country

habitats %>%
  group_by(Country, Zone) %>%
  summarise(H = diversity(Area, index = "shannon", 2)) -> rich

div_map <- merge(world, filter(rich, Zone == "Coastal"), by = "Country")

mapview(div_map, zcol = "H")

```


Oceanic habitats diversity
=======================================================================

Column {data-height=700}
-----------------------------------------------------------------------
### Oceanic habitats diversity calculated with Shannon index (H)

```{r Oceanic habitat diversity, fig.height=10}
# habitat diversity per country
habitats %>%
  group_by(Country, Zone) %>%
  summarise(H = diversity(Area, index = "shannon", 2)) -> rich

div_map <- merge(world, filter(rich, Zone == "Oceanic"), by = "Country")
library(mapview)

mapview(div_map, zcol = "H")

```